Awesome GitHub RepositoriesGraph-Based Workflow Orchestrators

Auto-GPT is an autonomous agent framework designed for creating and deploying AI agents that use large language models to plan and execute complex goals independently. The system provides a comprehensive environment for managing the entire agent lifecycle, from initial design and testing to live production deployment. The project features a low-code workflow designer that allows users to define agent behaviors by connecting functional blocks in a visual interface. It includes an agent marketplace for discovering and deploying pre-configured agent templates and a standardized evaluation tool t

Langflow is a low-code platform for designing and deploying multi-step AI agent pipelines and large language model sequences. It provides a visual environment to map logic and data flow between components, serving as an orchestrator for managing conversations and data retrieval across multiple autonomous agents. The platform distinguishes itself through a drag-and-drop interface that allows for the construction of complex AI pipelines without extensive boilerplate code. It enables the conversion of these internal workflows into standardized tools for external connectivity via the Model Contex

This project provides a comprehensive framework for building, training, and managing autonomous agents. It enables the construction of systems that utilize language models to plan, manage memory, and execute multi-step tasks through iterative reasoning loops and tool-based actions. The framework distinguishes itself by offering specialized capabilities for interacting with graphical user interfaces and legacy software, allowing agents to perceive visual elements and perform actions like a human user. It supports complex, cross-application workflows through graph-based orchestration and provid

This project provides an advanced English curriculum and a set of instructional guides designed to help non-native speakers move from intermediate to advanced proficiency. It functions as a guide for AI-powered language training, utilizing structured workflows and prompt engineering with large language models to facilitate self-directed study. The system implements AI workflow orchestration, chaining different artificial intelligence models into feedback loops to automate linguistic exercises and corrections. This approach combines multiple AI specializations to coordinate training across lis

CrewAI is a multi-agent orchestration framework and autonomous agent workflow engine. It provides a system for coordinating autonomous AI agents with specific roles and goals to solve complex tasks through collaborative intelligence. The framework distinguishes itself through a collaborative AI agent system that enables multiple language model instances to share intelligence and execute multi-step objectives via role-playing. It incorporates human-in-the-loop mechanisms, allowing for manual review checkpoints to validate decisions and refine outcomes within autonomous execution paths. The pl

Scrapegraph-ai is a Python framework that uses large language models to automate the extraction of structured data from websites and documents. It functions as an AI-driven data extraction pipeline that converts unstructured web content into structured formats using natural language processing and graph-based logic. The project utilizes graph-based task orchestration to model scraping workflows as interconnected nodes. It features a pluggable model interface for connecting to cloud or local artificial intelligence providers and can generate executable Python code on the fly to handle site-spe

Redux-Saga is a middleware library for Redux applications that manages asynchronous data flows and complex side effects. It serves as a decoupled state management effect layer and workflow orchestrator, utilizing JavaScript generator functions to pause and resume asynchronous operations without blocking the application. The library distinguishes itself by using generators to manage sequential or parallel tasks and state transitions outside of the main user interface thread. This approach allows for the coordination of complex asynchronous workflows, such as multi-step data fetching and API ca

Guidance is a control framework and generation orchestrator for large language models. It provides a programming layer to steer model outputs through structured templates, schema enforcement, and logical flow management. The framework distinguishes itself by interleaving model generation with local code execution, enabling the use of loops and conditional branching within a single session. It employs grammar-based token constraints and regular expressions to force models to sample only from tokens that satisfy a specific structural format, ensuring strict adherence to predefined data models.

Rasa is a chatbot development platform and conversational AI framework used to design, deploy, and integrate multi-turn conversational agents. It functions as an LLM orchestration engine and NLU dialogue manager, combining large language model fluency with structured business logic to control agent behavior. The framework enables the development of conversational assistants that automate text and voice interactions. It allows for the definition of conversational flows using flexible sequences and provides tools to inspect agent decisions to debug and validate the internal reasoning process.

Open-Higgsfield-AI is a generative AI content studio and visual workflow orchestrator. It provides a unified interface for creating photorealistic images and videos, utilizing a node-based editor to chain multiple image, video, and audio models into automated content pipelines. The system functions as an AI video animation tool and local GPU inference engine, allowing users to run generative models on local hardware or remote servers. It includes specialized capabilities for audio-driven lip synchronization and cinematic camera controls to adjust virtual lens and focal settings. The platform

Coze Studio is a development platform for building intelligent agents and conversational applications. It provides a visual environment where users construct agents by linking workflows, knowledge bases, and custom prompts to automate complex tasks. The system functions as a central hub for managing AI model services, allowing developers to connect various providers to serve as the intelligence layer for their applications. The platform distinguishes itself through a node-based workflow orchestrator that enables the design of automated logic sequences on a visual canvas. It includes a modular

This project is an agentic workflow orchestrator designed for building and deploying autonomous systems that perform multi-step reasoning. It functions as a tool-augmented engine, enabling developers to chain model calls with external function execution to complete complex, user-defined tasks. By integrating large language models with persistent memory and stateful logic, the framework supports the creation of intelligent applications capable of independent operation. The platform distinguishes itself through graph-based state orchestration, which allows developers to define logic steps and t

This project is a Python library designed for building, testing, and deploying autonomous agents that execute complex workflows. It functions as a multi-agent orchestration framework, enabling the creation of systems where specialized agents communicate, delegate tasks, and integrate with external services to complete multi-step automated processes. The framework distinguishes itself by combining deterministic code execution with adaptive language model reasoning. It utilizes structured graph-based logic and state-machine execution to maintain persistent context across multi-turn interactions

LangChain.js is a framework for building, executing, and monitoring stateful agentic applications. It provides an orchestration engine that models workflows as directed graphs, allowing developers to connect language models, data sources, and external tools into modular, multi-step processes. The platform distinguishes itself through its focus on stateful execution and human-in-the-loop control. It manages agent lifecycles by persisting execution state across threads, enabling fault tolerance and the ability to pause workflows at designated breakpoints for manual review or modification. This

This project is a comprehensive framework for developing, orchestrating, and deploying autonomous agents. It provides a structured environment for building agents that utilize reasoning loops to perform multi-step tasks, manage state through graph-based workflows, and interact with external tools. By mapping unstructured model outputs into typed schemas, the framework ensures reliable integration with downstream application logic. The platform distinguishes itself through a focus on production-grade reliability and security. It incorporates hybrid memory systems that combine vector embeddings

This project is a retrieval-augmented generation application designed to answer questions from uploaded PDF documents. It functions as a document question-answering engine and a streaming AI chat interface that provides responses backed by specific source citations. The system utilizes a state-machine workflow orchestrator to coordinate multi-step document ingestion and retrieval pipelines. This orchestration allows for step-by-step visualization and debugging of the process as documents are parsed and processed. The application manages the full lifecycle of document interaction, including P

This project is a comprehensive suite of AI tools and frameworks, featuring an LLM multi-agent orchestrator, an autonomous agent runtime, and a stateful application framework. It provides the infrastructure to build and manage specialized AI agents capable of coordinating complex tasks through graph-based workflows and shared state. The system is distinguished by its implementation of the Model Context Protocol, allowing for standardized resource discovery and communication between AI clients and servers. It further includes an AI-powered documentation generator designed to analyze source cod

Autoware is an open-source autonomous driving software platform built on the robotics middleware standard. It provides a comprehensive stack for managing perception, planning, and control, enabling the development and deployment of full-stack autonomous driving software on commercial transport hardware. The platform utilizes a component-based modular architecture that organizes driving functions into isolated, interchangeable nodes. This design is supported by a hardware-abstraction layer and plugin-based sensor integration, which allow the software to interface with diverse hardware configur

Spider-flow is a Java-based web crawling and data extraction platform that provides a centralized environment for managing automated information gathering. It functions as a no-code tool, allowing users to define complex data collection pipelines through a visual, drag-and-drop interface rather than manual programming. The platform distinguishes itself through a graph-based workflow orchestration system where users link discrete nodes to define navigation and parsing logic. It supports dynamic content crawling by integrating headless browsers to execute JavaScript and render page content that

Promptflow este un framework de dezvoltare și un orchestrator pentru construirea de aplicații bazate pe modele de limbaj mari (LLM). Acesta funcționează ca o suită de instrumente pentru proiectarea, orchestrarea și implementarea fluxurilor de lucru AI prin conectarea prompt-urilor, a codului Python personalizat și a modelelor de limbaj în secvențe executabile. Proiectul se distinge printr-un designer vizual de fluxuri de lucru AI care permite crearea de grafuri aciclice direcționate (DAG) de noduri logice. Oferă un mediu dedicat de prompt engineering pentru versionarea și compararea șabloanelor, alături de trasarea execuției cu stare pentru a înregistra apelurile de funcții și valorile variabilelor în vederea depanării pas cu pas. Platforma acoperă o gamă largă de capabilități, inclusiv RAG (Retrieval Augmented Generation) prin căutări în baze de date vectoriale și pipeline-uri de evaluare bazate pe metrici pentru testare în loturi și asigurarea calității. Gestionează întregul ciclu de viață, de la dezvoltare la producție, prin implementare containerizată, servirea endpoint-urilor de flux de lucru și gestionarea securizată a conexiunilor pentru credențialele API. Sunt furnizate o interfață în linie de comandă (CLI) și un SDK pentru validarea fluxurilor de lucru și integrarea în pipeline-uri CI/CD automatizate.